Systems such as intelligence collecting systems, electronic countermeasure systems, and electronic support measures systems generally employ a wireless receiver. With the proliferation of wireless technology, such receivers typically receive many continuous wave and pulse signals (e.g., tens, hundreds or thousands, simultaneously) from different sources (commonly referred to as emitters) which are transmitting in the receiver's pass-band. Thus, the receiver must distinguish signals-of-interest from other signals, which requires separation of the individual signals. One effective means of separation of signals from different emitters is via identification of the pulse repetition interval (PRI) of pulses from each emitter transmission. There are many possible types of PRI patterns, ranging from a simple continuous wave signal, to stable (linear pattern), patterned (a repeating non-linear pattern), or random (no apparent pattern presented).
While identifying continuous waves is relatively simple, identifying complex PRI patterns is not trivial. Detection of PRI patterns has applications, for example, in the military arena, such as intelligence gathering missions, electronic countermeasures, and targeting. Likewise detection of PRI patterns has applications in the civilian arena, such as homeland security and police based intelligence gathering, and detecting the presence of interfering transmissions that may adversely affect air traffic control systems (e.g., jammers or spoofers).
Existing systems capable of PRI pattern recognition perform one or more aspects of known PRI deinterleaving and identification functions, of which there are many. Most of these functions are expert-system based, and look for pattern matches with respect to difference in times-of-arrival (TOA) of the measured input pulse data stream. Such conventional techniques are relatively slow in their execution times, as they have to perform exhaustive analysis of the input data to detect many, if not all, of the known PRI pattern forms that can be presented to and measured by the system. Thus, overall latency to detecting and reporting the presence of all emitters is relatively high. This latency is of particular concern when hostile or otherwise threatening emitters are present.
What is needed, therefore, are low latency techniques that identify PRI patterns within a collected pulse data, and associate pulses with the identified PRI patterns.